In this episode, Nathan sits down with Daniel Kang, Assistant Professor of Computer Science at the University of Illinois. Kang has done pioneering work bringing zero knowledge cryptographic proofs to AI. In this episode, they chat about the cryptographic theory behind Daniel's work, how cryptography allows us to balance the tradeoff between privacy and authenticity, and how cryptography usage is needed in a world where LLMs are increasingly embedded into our daily lives. If you're looking for an ERP platform, check out our sponsor, NetSuite: http://netsuite.com/cognitive

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TIMESTAMPS:

(00:00) Episode Preview

(00:01:04) Nathan's Introduction

(00:07:06) Motivation for bringing zero-knowledge proofs to AI

(00:10:27) Verifying model execution without revealing model details

(00:12:42) Verifying medical AI services haven't been tampered with

(00:15:09) Sponsors: Netsuite | Omneky

(00:18:54) Cryptographic hashes for commitments

(00:22:42) Assumptions underlying cryptographic hashes 

(00:25:20) Adding entropy through salting 

(00:26:24) Z case snarks and the proving process 

(00:31:00) Using lookup tables for nonlinearities

(00:33:35) Floating point vs fixed point calculations 

(00:34:08) Quantizing models for efficiency

(00:35:55) Using polynomials to represent arbitrary computations

(00:37:26) What are finite fields? 

(00:41:23) Toxic waste for cryptographic secrecy

(00:45:51) Computational costs

(00:47:39) The experience of using a cryptography application to verify model output  

(00:49:05) Verification key doesn't reveal model weights

(00:56:36) What using crypto infrastructure in AI enables and challenges to its implementation 

(01:01:26) Potential for 10-100x cost reductions

(01:04:51) Authenticating images with attested cameras

(01:11:56) How cryptography in AI could impact daily life

(01:14:25) On-device credential verification

(01:15:50) Potential for regulation of hardware authentication

(01:18:52) Upcoming work to reduce proof costs

LINKS:

Daniel's website

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Music Credit: GoogleLM